Dead reckoning is the process of updating a known absolute position during travel by using relative movement information like speed and angular velocity of a motor vehicle, robot, aircraft, ship, etc. Such relative movement information is typically provided by relative sensors like odometers, airspeed indicators, logs and gyroscopes. Odometer data, airspeed indicator data and log data are indicative of a covered distance d, while gyroscope data are indicative of a change of direction Δφ and, therefore, the course during travel. Once an absolute position and heading of a vehicle is known (i.e., the geographic coordinates and heading of the position), its subsequent positions can be determined from these relative sensor data.
Modern positioning systems utilize an absolute position sensor such as a Global Positioning System (GPS) sensor to determine the current position during travel. The GPS sensor receives radio signals from different GPS satellites, and on basis of the measured signal runtimes the absolute position is determined. The determination of the vehicle position using the GPS sensor fails when not enough satellite signals are received (e.g., due to signal fading in tunnels or urban canyons). Moreover, the quality of the GPS signal can change very quickly, which is observable as position jumping around the real position.
In order to overcome such problems associated with absolute position sensors, modern positioning systems additionally utilize relative movement information from relative sensors for performing dead reckoning positioning during travel. In this context, the main task is an accurate calibration of the relative sensor data since the quality of the measured sensor data depends on the sensors used and external parameters, such as the ambient temperature. Moreover, the measured sensor data as such do not directly indicate the covered distance d and change of direction Δφ during travel. Accordingly, a transformation of the measured sensor data is required in order to perform dead reckoning positioning. This transformation is called “calibration” hereinafter, and the process of determining the current calibration parameters is called “learning”.
Common learning algorithms determine the calibration of odometer data and gyroscope data in separate steps and under well defined conditions. For example, in order to obtain a good odometer calibration, the movement of the vehicle during calibration should be as straight as possible. Deviations from a straight movement (e.g., by unintentionally driving a wiggly line) introduce an error in the calibration which is difficult to estimate and, therefore, difficult to compensate electronically. Further, the calibration of a gyroscope sensor requires a precise measurement of a course change of the vehicle. When using a GPS sensor for determining the course change, a gyroscope calibration with small errors can only be obtained when driving a straight line with high enough speed two times and performing a well defined turn in between.
Hence, the commonly used learning processes require some effort and time in order to obtain good calibration results. In addition, calibration errors are difficult to estimate and compensate when using the above described learning process. However, a good calibration with small calibration errors is crucial, since calibration errors are summed up during dead reckoning positioning, leading therefore to large deviations of the calculated absolute positions from the real positions.